big data

Big Data and Medicine {Comments Off on Big Data and Medicine}

By Vamsi R.

Big data is paving the road for great technological changes in many industries and giving researchers a powerful tool to analyze and focus on any area of improvement in their company or industry. The medical industry is greatly benefitting from the role of big data in the recent years and it has the potential to change the way the entire field operates.
The role of big data in medicine has no limitations. Beyond improving profits, “Big data in healthcare is being used to predict epidemics, cure disease, improve the quality of life and avoid preventable deaths” (Marr). This is revolutionary as it gives medical professionals a powerful tool in accessing a situation and making well-informed decisions. Big data helps by “aggregating more and more information around aggregating more and more information around multiple scales for what constitutes a disease – from the DNA, proteins, and metabolites to cells, tissues, organs, organisms, and ecosystems” (Mckinsey). This lowers the possibility of making a medical error as organized information is available to help you make the best decision in a particular situation. It also helps researches find cures and preventions for diseases in a more efficient way as large information is readily available in an organized manner.
Big data could also mean an end to the scientific method and create a streamline source for medical answers. It is claimed that “so much information will be available at our fingertips in the future, that there will be almost no need for experiments. The answers are already out there” (Standen). Using a large pool of medical records in the database, a researcher can see if one attribute is a causation for another. This will help them gain further understanding in their research without undergoing the long process of obtaining research grants and conducting independent studies.
There are many companies and organizations that are making the shift to using big data in their operations. One of these companies is IBM which has “announced an acquisition that will enable its Watson platform to learn from stored medical images” (Nelson). IBM’s strategy is to “combine rich image analytics with deep learning” to “cross reference the images with clinical studies, health records, and other sources”(Nelson). Using big data to transform their industry and improve quality overall is exciting to see in all aspects. With a company as large as IBM utilizing big data for medicine, it will not be so wrong to assume that big data might be the future for all healthcare and medical companies.
Organizing large amounts of information can save immense time and costs to medical professionals. With the entrance of big data, many technological changes are being seen in the healthcare and medical research industries. These changes are proving to be beneficial in giving medical professionals a powerful tool in making high quality decisions and have the scope to revolutionize the way the industry operates a whole. Overall, big data is greatly changing the medical field and will only grow in use by medical professionals in the future.

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Managing Big Data {2}

The exponential growth of computed data has become more and more difficult to manage.  Big data  refers to a collection of data that can be so large and complex, that traditional data management techniques cannot consistently manage due to the unpredictable variation in data.  Any average person today generates large amounts of data daily, with their mobile devices, computers, and Internet activity and behaviors.  All of these bits and details of data can add up to a significant amount.  Big data challenges are characterized by volume, velocity, and variety.  Volume is big data’s greatest challenge because even though some companies may be able to store vast amounts of data, they are not able to process it into meaningful information due to its sheer size.  Velocity is the “speed” in which the data flows.  Some organization’s servers cannot handle the increasing amount of demand.  In addition, large amounts of unstructured data such as photos, audio, and video have begun to flood in from the multitude of social networking outlets such as Facebook, Twitter, and YouTube, and are constantly streamed in real-time to billions of users.  Within the past decade, the significant increase in mobile products, such as cell phones and tablets, have also largely increased the demand for data services.  Lastly, the immense variety in data types make organizing and interpreting such data cumbersome.  Cisco forecasts that by 2017, annual global data center IP traffic will reach 7.7 zettabytes, or 8.26 billion terabytes.

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Big Big Data {1}

 Big Data is loosely used term in the database industry. At first this term is seen as so simple it becomes complicated or might be used so general it becomes ineffective. Analyzing Big Data is essential in well-established businesses to help grow and mold the company. Big Data as described by Forbes is “ a collection of data from traditional and digital sources inside and outside your company that represents a source for ongoing discovery and analysis.” (What is Big Data) With the expansion of the information and technology age this becomes more and more relevant everyday. There are even organizations that companies can outsource to do this type of analysis for them. Big Data is a relatively new idea and has been becoming more efficient but, there can still be improvement.

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Data as a new Markets {3}

In the article ” Data Markets: The Emerging Data Economy” written by Gil Elbaz, the author talks about how people turn data into a new market where they collect, analyze and sell it to the market. There are advantages for both parties. One can make money out of it, the other can use the data and don’t have to maintain it. The author also give 2 examples of data markets like: Jigsaw and Kaggle. Jigsaw is a collection of contact information collect from individual and organization. On the other hand, Kaggle is more of a community where company provide the data and people from around the world join and analyze the data and make prediction, find pattern or whatever the goal of the project is. And in return, these contributor will get a reward.

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Size of Facebook’s Data {4}

The article that I chose to talk about this week is called “How Big Is Facebook’s Data? 2.5 Billion Pieces of Content and 500+ Terabytes Ingested Every Day”, by Josh Constine. The title says it all. Facebook revealed to reporters that their system processes over 2.5 billion pieces of content worth 500+ terabytes of data per day. The author talks about how the company system processes approximately 2.7 billion ‘Like’ actions and 300 million photos per day. The Vice President of Engineering, Jay Parikh, revealed that over 100 petebytes of data are stored in their data warehouse. In order for Facebook to support data-intensive activities and distributed applications, they use a software framework called Apache Hadoop. Hadoop provides very large bandwidth across the cluster and enables applications to process petabytes of data and thousands of independent computers. Parikh said to the reporters that Facebook operates the single largest Hadoop system in the world; one that’s even larger than Yahoo’s.

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Implementing SQL 2012 for Big Data {4}

In this article, the author discusses about how Microsoft released a new version of SQL Server 2012. With this update, SQL Server 2012 can help organizations analyze large amounts of data otherwise known as big data. Microsoft is promoting SQL 2012 as a very useful tool to observe big data and act as a link between unstructured data platforms and data warehouse based tools. An early user of SQL 2012 has to process about 350 GB of social networking data a day, by using SQL, SQL queries billions of rows of data in seconds and allows the user to iterate and test a large number of scenarios in a short amount of time.

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Old Fashion SQL {4}

The article I chose to blog about this week was “Google App Engine Goes Old School With SQL Database” written by Caleb Garling of Wired.com. This article speaks about the addition of a SQL database to their Google App Engine. The Google App Engine is a means for Google customers to build and host applications on top of Google’s online infrastructure. Prior to this, Google was in the forefront of the NoSQL movement, but with this announcement, it shows that good old fashion SQL is alive and well. Google provides this SQL database so people can power their App Engine applications with a relational database, which will be more familiar to the masses, in a “fully managed cloud environment”(Garling, 2011).  Google is totally headed in the opposite direction of competitor Oracle which announced their Big Data Appliance (NoSQL database).

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From Big Data to Big Impact {2}

I picked the article called Business Intelligence and analytics: from big data to big impact that talks about how these areas of study was created due to the urge to manage the excessive amount of data being created and the need to classify and analyze this data to use it as efficient as possible. According to the MIS Quartely after conducting a survey with over 4,000 IT professionals from about 93 counties in different industries the IBM tech reported in 2011 that business analytics is one of the four major technology trends in 2010. It also mentions that McKinsey Global Institute predicted that the United States will have a shortage of people with well-developed analytical skills by 2018.

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The Big 5 of Big Data {3}

The article I chose to blog about this week is,” Big Data Right Now: Five Trendy Open Source Technologies” by Mr. Tim Gasper of TechCrunch.com. The article starts of by saying Big Data is on everyones mind, and companies “will have spent $4.3 billion on Big Data technologies by the end of 2012″ (Gasper, 2012). However,  author believes this is just the tip of the iceberg and states these initial investments will cause a chain reaction for upwards of $34 Billion in spendings through 2013. The field is so expansive, and there are so many players in Big Data (with more to come) the author provided a picture to show just how big this field is.

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Why relational databases make sense for big data? {2}

In the article  ” Why relational databases make sense for big data”, Dave Rosenberg talked about the “big data” trend that more and more organizations are now (or soon will be) dealing with managing and extracting information from databases that are growing into the multi-petabyte range. This trend caused developers are forced to seek new “NoSQL” approaches and instead process data in a distributed manner. These so called “NoSQL” such as Cassandra and MongoDB databases, are built to scale easily and handle massive amounts of data in a highly fluid manner. Dave stated himself as a NoSQL supporter but he also pointed out that there is often a point where all of this data needs to be aggregated and parsed for different reasons, in a more traditional SQL data model.

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